algo.generic

Generic arithmetic interface
This library defines generic versions of + - * / as multimethods
that can be defined for any type. The minimal required
implementations for a type are binary + and * plus unary - and /.
Everything else is derived from these automatically. Explicit
binary definitions for - and / can be provided for
efficiency reasons.

Generic comparison interface
This library defines generic versions of = not= < > <= >= zero?
as multimethods that can be defined for any type. Of the
greater/less-than relations, types must minimally implement >.

A Pretty Printer for Clojure
clojure.pprint implements a flexible system for printing structured data
in a pleasing, easy-to-understand format. Basic use of the pretty printer is
simple, just call pprint instead of println. More advanced users can use
the building blocks provided to create custom output formats.
Out of the box, pprint supports a simple structured format for basic data
and a specialized format for Clojure source code. More advanced formats,
including formats that don't look like Clojure data at all like XML and
JSON, can be rendered by creating custom dispatch functions.
In addition to the pprint function, this module contains cl-format, a text
formatting function which is fully compatible with the format function in
Common Lisp. Because pretty printing directives are directly integrated with
cl-format, it supports very concise custom dispatch. It also provides
a more powerful alternative to Clojure's standard format function.
See documentation for pprint and cl-format for more information or
complete documentation on the the clojure web site on github.

Reflection on Host Types
Alpha - subject to change.
Two main entry points:
* type-reflect reflects on something that implements TypeReference.
* reflect (for REPL use) reflects on the class of an instance, or
on a class if passed a class
Key features:
* Exposes the read side of reflection as pure data. Reflecting
on a type returns a map with keys :bases, :flags, and :members.
* Canonicalizes class names as Clojure symbols. Types can extend
to the TypeReference protocol to indicate that they can be
unambiguously resolved as a type name. The canonical format
requires one non-Java-ish convention: array brackets are <>
instead of [] so they can be part of a Clojure symbol.
* Pluggable Reflectors for different implementations. The default
JavaReflector is good when you have a class in hand, or use
the AsmReflector for "hands off" reflection without forcing
classes to load.
Platform implementers must:
* Create an implementation of Reflector.
* Create one or more implementations of TypeReference.
* def default-reflector to be an instance that satisfies Reflector.

Clojure String utilities
It is poor form to (:use clojure.string). Instead, use require
with :as to specify a prefix, e.g.
(ns your.namespace.here
(:require [clojure.string :as str]))
Design notes for clojure.string:
1. Strings are objects (as opposed to sequences). As such, the
string being manipulated is the first argument to a function;
passing nil will result in a NullPointerException unless
documented otherwise. If you want sequence-y behavior instead,
use a sequence.
2. Functions are generally not lazy, and call straight to host
methods where those are available and efficient.
3. Functions take advantage of String implementation details to
write high-performing loop/recurs instead of using higher-order
functions. (This is not idiomatic in general-purpose application
code.)
4. When a function is documented to accept a string argument, it
will take any implementation of the correct *interface* on the
host platform. In Java, this is CharSequence, which is more
general than String. In ordinary usage you will almost always
pass concrete strings. If you are doing something unusual,
e.g. passing a mutable implementation of CharSequence, then
thread-safety is your responsibility.

by Stuart Sierra, with contributions and suggestions by
Chas Emerick, Allen Rohner, and Stuart Halloway

A unit testing framework.
ASSERTIONS
The core of the library is the "is" macro, which lets you make
assertions of any arbitrary expression:
(is (= 4 (+ 2 2)))
(is (instance? Integer 256))
(is (.startsWith "abcde" "ab"))
You can type an "is" expression directly at the REPL, which will
print a message if it fails.
user> (is (= 5 (+ 2 2)))
FAIL in (:1)
expected: (= 5 (+ 2 2))
actual: (not (= 5 4))
false
The "expected:" line shows you the original expression, and the
"actual:" shows you what actually happened. In this case, it
shows that (+ 2 2) returned 4, which is not = to 5. Finally, the
"false" on the last line is the value returned from the
expression. The "is" macro always returns the result of the
inner expression.
There are two special assertions for testing exceptions. The
"(is (thrown? c ...))" form tests if an exception of class c is
thrown:
(is (thrown? ArithmeticException (/ 1 0)))
"(is (thrown-with-msg? c re ...))" does the same thing and also
tests that the message on the exception matches the regular
expression re:
(is (thrown-with-msg? ArithmeticException #"Divide by zero"
(/ 1 0)))
DOCUMENTING TESTS
"is" takes an optional second argument, a string describing the
assertion. This message will be included in the error report.
(is (= 5 (+ 2 2)) "Crazy arithmetic")
In addition, you can document groups of assertions with the
"testing" macro, which takes a string followed by any number of
assertions. The string will be included in failure reports.
Calls to "testing" may be nested, and all of the strings will be
joined together with spaces in the final report, in a style
similar to RSpec <http://rspec.info/>
(testing "Arithmetic"
(testing "with positive integers"
(is (= 4 (+ 2 2)))
(is (= 7 (+ 3 4))))
(testing "with negative integers"
(is (= -4 (+ -2 -2)))
(is (= -1 (+ 3 -4)))))
Note that, unlike RSpec, the "testing" macro may only be used
INSIDE a "deftest" or "with-test" form (see below).
DEFINING TESTS
There are two ways to define tests. The "with-test" macro takes
a defn or def form as its first argument, followed by any number
of assertions. The tests will be stored as metadata on the
definition.
(with-test
(defn my-function [x y]
(+ x y))
(is (= 4 (my-function 2 2)))
(is (= 7 (my-function 3 4))))
As of Clojure SVN rev. 1221, this does not work with defmacro.
See http://code.google.com/p/clojure/issues/detail?id=51
The other way lets you define tests separately from the rest of
your code, even in a different namespace:
(deftest addition
(is (= 4 (+ 2 2)))
(is (= 7 (+ 3 4))))
(deftest subtraction
(is (= 1 (- 4 3)))
(is (= 3 (- 7 4))))
This creates functions named "addition" and "subtraction", which
can be called like any other function. Therefore, tests can be
grouped and composed, in a style similar to the test framework in
Peter Seibel's "Practical Common Lisp"
<http://www.gigamonkeys.com/book/practical-building-a-unit-test-framework.html>
(deftest arithmetic
(addition)
(subtraction))
The names of the nested tests will be joined in a list, like
"(arithmetic addition)", in failure reports. You can use nested
tests to set up a context shared by several tests.
RUNNING TESTS
Run tests with the function "(run-tests namespaces...)":
(run-tests 'your.namespace 'some.other.namespace)
If you don't specify any namespaces, the current namespace is
used. To run all tests in all namespaces, use "(run-all-tests)".
By default, these functions will search for all tests defined in
a namespace and run them in an undefined order. However, if you
are composing tests, as in the "arithmetic" example above, you
probably do not want the "addition" and "subtraction" tests run
separately. In that case, you must define a special function
named "test-ns-hook" that runs your tests in the correct order:
(defn test-ns-hook []
(arithmetic))
Note: test-ns-hook prevents execution of fixtures (see below).
OMITTING TESTS FROM PRODUCTION CODE
You can bind the variable "*load-tests*" to false when loading or
compiling code in production. This will prevent any tests from
being created by "with-test" or "deftest".
FIXTURES
Fixtures allow you to run code before and after tests, to set up
the context in which tests should be run.
A fixture is just a function that calls another function passed as
an argument. It looks like this:
(defn my-fixture [f]
Perform setup, establish bindings, whatever.
(f) Then call the function we were passed.
Tear-down / clean-up code here.
)
Fixtures are attached to namespaces in one of two ways. "each"
fixtures are run repeatedly, once for each test function created
with "deftest" or "with-test". "each" fixtures are useful for
establishing a consistent before/after state for each test, like
clearing out database tables.
"each" fixtures can be attached to the current namespace like this:
(use-fixtures :each fixture1 fixture2 ...)
The fixture1, fixture2 are just functions like the example above.
They can also be anonymous functions, like this:
(use-fixtures :each (fn [f] setup... (f) cleanup...))
The other kind of fixture, a "once" fixture, is only run once,
around ALL the tests in the namespace. "once" fixtures are useful
for tasks that only need to be performed once, like establishing
database connections, or for time-consuming tasks.
Attach "once" fixtures to the current namespace like this:
(use-fixtures :once fixture1 fixture2 ...)
Note: Fixtures and test-ns-hook are mutually incompatible. If you
are using test-ns-hook, fixture functions will *never* be run.
SAVING TEST OUTPUT TO A FILE
All the test reporting functions write to the var *test-out*. By
default, this is the same as *out*, but you can rebind it to any
PrintWriter. For example, it could be a file opened with
clojure.java.io/writer.
EXTENDING TEST-IS (ADVANCED)
You can extend the behavior of the "is" macro by defining new
methods for the "assert-expr" multimethod. These methods are
called during expansion of the "is" macro, so they should return
quoted forms to be evaluated.
You can plug in your own test-reporting framework by rebinding
the "report" function: (report event)
The 'event' argument is a map. It will always have a :type key,
whose value will be a keyword signaling the type of event being
reported. Standard events with :type value of :pass, :fail, and
:error are called when an assertion passes, fails, and throws an
exception, respectively. In that case, the event will also have
the following keys:
:expected The form that was expected to be true
:actual A form representing what actually occurred
:message The string message given as an argument to 'is'
The "testing" strings will be a list in "*testing-contexts*", and
the vars being tested will be a list in "*testing-vars*".
Your "report" function should wrap any printing calls in the
"with-test-out" macro, which rebinds *out* to the current value
of *test-out*.
For additional event types, see the examples in the code.

This file defines a generic tree walker for Clojure data
structures. It takes any data structure (list, vector, map, set,
seq), calls a function on every element, and uses the return value
of the function in place of the original. This makes it fairly
easy to write recursive search-and-replace functions, as shown in
the examples.
Note: "walk" supports all Clojure data structures EXCEPT maps
created with sorted-map-by. There is no (obvious) way to retrieve
the sorting function.

A library for reduction and parallel folding. Alpha and subject
to change. Note that fold and its derivatives require Java 7+ or
Java 6 + jsr166y.jar for fork/join support. See Clojure's pom.xml for the
dependency info.

clojure.test extension for JUnit-compatible XML output.
JUnit (http://junit.org/) is the most popular unit-testing library
for Java. As such, tool support for JUnit output formats is
common. By producing compatible output from tests, this tool
support can be exploited.
To use, wrap any calls to clojure.test/run-tests in the
with-junit-output macro, like this:
(use 'clojure.test)
(use 'clojure.test.junit)
(with-junit-output
(run-tests 'my.cool.library))
To write the output to a file, rebind clojure.test/*test-out* to
your own PrintWriter (perhaps opened using
clojure.java.io/writer).

clojure.test extensions for the Test Anything Protocol (TAP)
TAP is a simple text-based syntax for reporting test results. TAP
was originally developed for Perl, and now has implementations in
several languages. For more information on TAP, see
http://testanything.org/ and
http://search.cpan.org/~petdance/TAP-1.0.0/TAP.pm
To use this library, wrap any calls to
clojure.test/run-tests in the with-tap-output macro,
like this:
(use 'clojure.test)
(use 'clojure.test.tap)
(with-tap-output
(run-tests 'my.cool.library))

core.async

Facilities for async programming and communication.
go blocks are dispatched over an internal thread pool, which
defaults to 8 threads. The size of this pool can be modified using
the Java system property `clojure.core.async.pool-size`.

core.async HIGHLY EXPERIMENTAL feature exploration
Caveats:
1. Everything defined in this namespace is experimental, and subject
to change or deletion without warning.
2. Many features provided by this namespace are highly coupled to
implementation details of core.async. Potential features which
operate at higher levels of abstraction are suitable for inclusion
in the examples.
3. Features provided by this namespace MAY be promoted to
clojure.core.async at a later point in time, but there is no
guarantee any of them will.

core.match

core.memoize

core.memoize is a memoization library offering functionality above Clojure's core `memoize`
function in the following ways:
**Pluggable memoization**
core.memoize allows for different back-end cache implmentations to be used as appropriate without
changing the memoization modus operandi.
**Manipulable memoization**
Because core.memoize allows you to access a function's memoization store, you do interesting things like
clear it, modify it, and save it for later.

core.rrb-vector

An implementation of the confluently persistent vector data
structure introduced in Bagwell, Rompf, "RRB-Trees: Efficient
Immutable Vectors", EPFL-REPORT-169879, September, 2011.
RRB-Trees build upon Clojure's PersistentVectors, adding logarithmic
time concatenation and slicing.
The main API entry points are clojure.core.rrb-vector/catvec,
performing vector concatenation, and clojure.core.rrb-vector/subvec,
which produces a new vector containing the appropriate subrange of
the input vector (in contrast to clojure.core/subvec, which returns
a view on the input vector).
core.rrb-vector's vectors can store objects or unboxed primitives.
The implementation allows for seamless interoperability with
clojure.lang.PersistentVector, clojure.core.Vec (more commonly known
as gvec) and clojure.lang.APersistentVector$SubVector instances:
clojure.core.rrb-vector/catvec and clojure.core.rrb-vector/subvec
convert their inputs to clojure.core.rrb-vector.rrbt.Vector
instances whenever necessary (this is a very fast constant time
operation for PersistentVector and gvec; for SubVector it is O(log
n), where n is the size of the underlying vector).
clojure.core.rrb-vector also exports its own versions of vector and
vector-of and vec which always produce
clojure.core.rrb-vector.rrbt.Vector instances. Note that vector-of
accepts :object as one of the possible type arguments, in addition
to keywords naming primitive types.

Extensible languages in Clojure, a la Racket's #lang.
This is a simple library that monkey patches clojure.core/load
to be extensible to different backends.
`monkey-patch-extensible-load` does the actual monkey-patching and
must be called explicitly.
`lang-dispatch` is a map from keywords to alternative `load` functions
(of type [String -> nil]). The corresponding function will be used to
load a file according its :lang metadata entry in the `ns` form.
To add a new implementation, use
(alter-var-root lang-dispatch assoc :new-impl my-load)
eg. A file with a `ns` form
(ns fancy-ns-form
{:lang :new-impl})
will use `my-load` to load the file.

core.unify

data.avl

An implementation of persistent sorted maps and sets based on AVL
trees which can be used as drop-in replacements for Clojure's
built-in sorted maps and sets based on red-black trees. Apart from
the standard sorted collection API, the provided map and set types
support the transients API and several additional logarithmic time
operations: rank queries via clojure.core/nth (select element by
rank) and clojure.data.avl/rank-of (discover rank of element),
"nearest key" lookups via clojure.data.avl/nearest, splits by key
and index via clojure.data.avl/split-key and
clojure.data.avl/split-at, respectively, and subsets/submaps using
clojure.data.avl/subrange.

data.priority-map

A priority map is very similar to a sorted map, but whereas a sorted map produces a
sequence of the entries sorted by key, a priority map produces the entries sorted by value.
In addition to supporting all the functions a sorted map supports, a priority map
can also be thought of as a queue of [item priority] pairs. To support usage as
a versatile priority queue, priority maps also support conj/peek/pop operations.
The standard way to construct a priority map is with priority-map:
user=> (def p (priority-map :a 2 :b 1 :c 3 :d 5 :e 4 :f 3))
#'user/p
user=> p
{:b 1, :a 2, :c 3, :f 3, :e 4, :d 5}
So :b has priority 1, :a has priority 2, and so on.
Notice how the priority map prints in an order sorted by its priorities (i.e., the map's values)
We can use assoc to assign a priority to a new item:
user=> (assoc p :g 1)
{:b 1, :g 1, :a 2, :c 3, :f 3, :e 4, :d 5}
or to assign a new priority to an extant item:
user=> (assoc p :c 4)
{:b 1, :a 2, :f 3, :c 4, :e 4, :d 5}
We can remove an item from the priority map:
user=> (dissoc p :e)
{:b 1, :a 2, :c 3, :f 3, :d 5}
An alternative way to add to the priority map is to conj a [item priority] pair:
user=> (conj p [:g 0])
{:g 0, :b 1, :a 2, :c 3, :f 3, :e 4, :d 5}
or use into:
user=> (into p [[:g 0] [:h 1] [:i 2]])
{:g 0, :b 1, :h 1, :a 2, :i 2, :c 3, :f 3, :e 4, :d 5}
Priority maps are countable:
user=> (count p)
6
Like other maps, equivalence is based not on type, but on contents.
In other words, just as a sorted-map can be equal to a hash-map,
so can a priority-map.
user=> (= p {:b 1, :a 2, :c 3, :f 3, :e 4, :d 5})
true
You can test them for emptiness:
user=> (empty? (priority-map))
true
user=> (empty? p)
false
You can test whether an item is in the priority map:
user=> (contains? p :a)
true
user=> (contains? p :g)
false
It is easy to look up the priority of a given item, using any of the standard map mechanisms:
user=> (get p :a)
2
user=> (get p :g 10)
10
user=> (p :a)
2
user=> (:a p)
2
Priority maps derive much of their utility by providing priority-based seq.
Note that no guarantees are made about the order in which items of the same priority appear.
user=> (seq p)
([:b 1] [:a 2] [:c 3] [:f 3] [:e 4] [:d 5])
Because no guarantees are made about the order of same-priority items, note that
rseq might not be an exact reverse of the seq. It is only guaranteed to be in
descending order.
user=> (rseq p)
([:d 5] [:e 4] [:c 3] [:f 3] [:a 2] [:b 1])
This means first/rest/next/for/map/etc. all operate in priority order.
user=> (first p)
[:b 1]
user=> (rest p)
([:a 2] [:c 3] [:f 3] [:e 4] [:d 5])
Priority maps support metadata:
user=> (meta (with-meta p {:extra :info}))
{:extra :info}
But perhaps most importantly, priority maps can also function as priority queues.
peek, like first, gives you the first [item priority] pair in the collection.
pop removes the first [item priority] from the collection.
(Note that unlike rest, which returns a seq, pop returns a priority map).
user=> (peek p)
[:b 1]
user=> (pop p)
{:a 2, :c 3, :f 3, :e 4, :d 5}
It is also possible to use a custom comparator:
user=> (priority-map-by > :a 1 :b 2 :c 3)
{:c 3, :b 2, :a 1}
Sometimes, it is desirable to have a map where the values contain more information
than just the priority. For example, let's say you want a map like:
{:a [2 :apple], :b [1 :banana], :c [3 :carrot]}
and you want to sort the map by the numeric priority found in the pair.
A common mistake is to try to solve this with a custom comparator:
(priority-map
(fn [[priority1 _] [priority2 _]] (< priority1 priority2))
:a [2 :apple], :b [1 :banana], :c [3 :carrot])
This will not work! In Clojure, like Java, all comparators must be total orders,
meaning that you can't have a tie unless the objects you are comparing are
in fact equal. The above comparator breaks that rule because
[2 :apple] and [2 :apricot] tie, but are not equal.
The correct way to construct such a priority map is by specifying a keyfn, which is used
to extract the true priority from the priority map's vals. (Note: It might seem a little odd
that the priority-extraction function is called a *key*fn, even though it is applied to the
map's values. This terminology is based on the docstring of clojure.core/sort-by, which
uses `keyfn` for the function which extracts the sort order.)
In the above example,
user=> (priority-map-keyfn first :a [2 :apple], :b [1 :banana], :c [3 :carrot])
{:b [1 :banana], :a [2 :apple], :c [3 :carrot]}
You can also combine a keyfn with a comparator that operates on the extracted priorities:
user=> (priority-map-keyfn-by
first >
:a [2 :apple], :b [1 :banana], :c [3 :carrot])
{:c [3 :carrot], :a [2 :apple], :b [1 :banana]}
All of these operations are efficient. Generally speaking, most operations
are O(log n) where n is the number of distinct priorities. Some operations
(for example, straightforward lookup of an item's priority, or testing
whether a given item is in the priority map) are as efficient
as Clojure's built-in map.
The key to this efficiency is that internally, not only does the priority map store
an ordinary hash map of items to priority, but it also stores a sorted map that
maps priorities to sets of items with that priority.
A typical textbook priority queue data structure supports at the ability to add
a [item priority] pair to the queue, and to pop/peek the next [item priority] pair.
But many real-world applications of priority queues require more features, such
as the ability to test whether something is already in the queue, or to reassign
a priority. For example, a standard formulation of Dijkstra's algorithm requires the
ability to reduce the priority number associated with a given item. Once you
throw persistence into the mix with the desire to adjust priorities, the traditional
structures just don't work that well.
This particular blend of Clojure's built-in hash sets, hash maps, and sorted maps
proved to be a great way to implement an especially flexible persistent priority queue.
Connoisseurs of algorithms will note that this structure's peek operation is not O(1) as
it would be if based upon a heap data structure, but I feel this is a small concession for
the blend of persistence, priority reassignment, and priority-sorted seq, which can be
quite expensive to achieve with a heap (I did actually try this for comparison). Furthermore,
this peek's logarithmic behavior is quite good (on my computer I can do a million
peeks at a priority map with a million items in 750ms). Also, consider that peek and pop
usually follow one another, and even with a heap, pop is logarithmic. So the net combination
of peek and pop is not much different between this versatile formulation of a priority map and
a more limited heap-based one. In a nutshell, peek, although not O(1), is unlikely to be the
bottleneck in your program.
All in all, I hope you will find priority maps to be an easy-to-use and useful addition
to Clojure's assortment of built-in maps (hash-map and sorted-map).

java.jdbc

A Clojure interface to SQL databases via JDBC
clojure.java.jdbc provides a simple abstraction for CRUD (create, read,
update, delete) operations on a SQL database, along with basic transaction
support. Basic DDL operations are also supported (create table, drop table,
access to table metadata).
Maps are used to represent records, making it easy to store and retrieve
data. Results can be processed using any standard sequence operations.
For most operations, Java's PreparedStatement is used so your SQL and
parameters can be represented as simple vectors where the first element
is the SQL string, with ? for each parameter, and the remaining elements
are the parameter values to be substituted. In general, operations return
the number of rows affected, except for a single record insert where any
generated keys are returned (as a map).
For more documentation, see:
http://clojure-doc.org/articles/ecosystem/java_jdbc/home.html

math.numeric-tower

Math functions that deal intelligently with the various
types in Clojure's numeric tower, as well as math functions
commonly found in Scheme implementations.
expt - (expt x y) is x to the yth power, returns an exact number
if the base is an exact number, and the power is an integer,
otherwise returns a double.
abs - (abs n) is the absolute value of n
gcd - (gcd m n) returns the greatest common divisor of m and n
lcm - (lcm m n) returns the least common multiple of m and n
When floor, ceil, and round are passed doubles, we just defer to
the corresponding functions in Java's Math library. Java's
behavior is somewhat strange (floor and ceil return doubles rather
than integers, and round on large doubles yields spurious results)
but it seems best to match Java's semantics. On exact numbers
(ratios and decimals), we can have cleaner semantics.
floor - (floor n) returns the greatest integer less than or equal to n.
If n is an exact number, floor returns an integer,
otherwise a double.
ceil - (ceil n) returns the least integer greater than or equal to n.
If n is an exact number, ceil returns an integer,
otherwise a double.
round - (round n) rounds to the nearest integer.
round always returns an integer. round rounds up for values
exactly in between two integers.
sqrt - Implements the sqrt behavior I'm accustomed to from PLT Scheme,
specifically, if the input is an exact number, and is a square
of an exact number, the output will be exact. The downside
is that for the common case (inexact square root), some extra
computation is done to look for an exact square root first.
So if you need blazingly fast square root performance, and you
know you're just going to need a double result, you're better
off calling java's Math/sqrt, or alternatively, you could just
convert your input to a double before calling this sqrt function.
If Clojure ever gets complex numbers, then this function will
need to be updated (so negative inputs yield complex outputs).
exact-integer-sqrt - Implements a math function from the R6RS Scheme
standard. (exact-integer-sqrt k) where k is a non-negative integer,
returns [s r] where k = s^2+r and k < (s+1)^2. In other words, it
returns the floor of the square root and the "remainder".

tools.emitter.jvm

tools.logging

Logging macros which delegate to a specific logging implementation. At
runtime a specific implementation is selected from, in order, slf4j,
Apache commons-logging, log4j, and finally java.util.logging.
The logging implementation can be expliticly determined by using
binding or alter-var-root to change the value of *logger-factory* to
another implementation of clojure.tools.logging.impl/LoggerFactory
(see also the *-factory functions in the impl namespace).

tools.macro

Local macros and symbol macros
Local macros are defined by a macrolet form. They are usable only
inside its body. Symbol macros can be defined globally
(defsymbolmacro) or locally (symbol-macrolet). A symbol
macro defines a form that replaces a symbol during macro
expansion. Function arguments and symbols bound in let
forms are not subject to symbol macro expansion.
Local macros are most useful in the definition of the expansion
of another macro, they may be used anywhere. Global symbol
macros can be used only inside a with-symbol-macros form.

Refactoring tool to move a Clojure namespace from one name/file to
another, and update all references to that namespace in your other
Clojure source files.
WARNING: This code is ALPHA and subject to change. It also modifies
and deletes your source files! Make sure you have a backup or
version control.

Dependency tracker which can compute which namespaces need to be
reloaded after files have changed. This is the low-level
implementation that requires you to find the namespace dependencies
yourself: most uses will interact with the wrappers in
clojure.tools.namespace.file and clojure.tools.namespace.dir or the
public API in clojure.tools.namespace.repl.